Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 551–560 of 6661 papers

Title	Date	Tasks	Status	Hype
Contrastive Collaborative Filtering for Cold-Start Item Recommendation	Feb 4, 2023	Collaborative FilteringContrastive Learning	CodeCode Available	1
Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations	Oct 26, 2022	Contrastive LearningSemantic Segmentation	CodeCode Available	1
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation	Mar 7, 2024	Continual LearningContrastive Learning	CodeCode Available	1
Biomedical Entity Linking with Contrastive Context Matching	Jun 14, 2021	ArticlesContrastive Learning	CodeCode Available	1
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth	Jul 12, 2021	AnatomyContrastive Learning	CodeCode Available	1
Blind Localization and Clustering of Anomalies in Textures	Apr 18, 2024	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Contrastive Cross-domain Recommendation in Matching	Dec 2, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks	Dec 30, 2021	ClusteringContrastive Learning	CodeCode Available	1
Contrastive Learning for Cross-Domain Open World Recognition	Mar 17, 2022	Contrastive Learning	CodeCode Available	1
Contrastive Learning of Musical Representations	Mar 17, 2021	Contrastive LearningLinear evaluation	CodeCode Available	1

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified